Predicting circulating biomarker response and its impact on the survival of advanced melanoma patients treated with adjuvant therapy

Advanced melanoma remains a disease with poor prognosis. Several serologic markers have been investigated to help monitoring and prognostication, but to date only lactate dehydrogenase (LDH) has been validated as a standard prognostic factor biomarker for this disease by the American Joint Committee on Cancer. In this work, we built a semi-mechanistic model to explore the relationship between the time course of several circulating biomarkers and overall or progression free survival in advanced melanoma patients treated with adjuvant high-dose interferon-[Formula: see text]. Additionally, due to the adverse interferon tolerability, a semi-mechanistic model describing the side effects of the treatment in the absolute neutrophil counts is proposed in order to simultaneously analyze the benefits and toxic effects of this treatment. The results of our analysis suggest that the relative change from baseline of LDH was the most significant predictor of the overall survival of the patients. Unfortunately, there was no significant difference in the proportion of patients with elevated serum biomarkers between the patients who recurred and those who remained free of disease. Still, we believe that the modelling framework presented in this work of circulating biomarkers and adverse effects could constitute an additional strategy for disease monitoring in advance melanoma patients

A systems pharmacology model for inflammatory bowel disease

Motivation The literature on complex diseases is abundant but not always quantitative. This is particularly so for Inflammatory Bowel Disease (IBD), where many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. We propose the elaboration and validation of a logic network for IBD able to capture the information available in the literature that will facilitate the identification/validation of therapeutic targets. Results In this article, we propose a logic model for Inflammatory Bowel Disease (IBD) which consists of 43 nodes and 298 qualitative interactions. The model presented is able to describe the pathogenic mechanisms of the disorder and qualitatively describes the characteristic chronic inflammation. A perturbation analysis performed on the IBD network indicates that the model is robust. Also, as described in clinical trials, a simulation of anti-TNFα, anti-IL2 and Granulocyte and Monocyte Apheresis showed a decrease in the Metalloproteinases node (MMPs), which means a decrease in tissue damage. In contrast, as clinical trials have demonstrated, a simulation of anti-IL17 and anti-IFNγ or IL10 overexpression therapy did not show any major change in MMPs expression, as corresponds to a failed therapy. The model proved to be a promising in silico tool for the evaluation of potential therapeutic targets, the identification of new IBD biomarkers, the integration of IBD polymorphisms to anticipate responders and non-responders and can be reduced and transformed in quantitative model/s

Development and implementation of novel methodologies to improve Pharmacometrics and Systems Pharmacology analysis

During the past decades Pharmacometrics and Systems Pharmacology (PSP) modelling has emerged as a promising discipline within drug development context. Model-based approaches in drug development involve the integration of pharmacokinetics (PK), pharmacodynamics (PD), disease progression and other relevant information to describe complex biological systems and the action of drugs by computational models. The use of such models can have a major impact during all phases of drug discovery and development and may ultimately result in significant cost reductions for the pharmaceutical industry. Modeling and simulation (M&S) in PSP integrates diverse scientific domains including pharmacology, mathematics, computer science, biostatistics, systems biology, and recently even artificial intelligence is being applied in this field. The diversity of this discipline sometimes results in the challenge that people of different backgrounds do not share the same knowledge about the different aspects governing M&S arena. The present thesis explores the possibility to improve standard PSP modelling by integrating different methodologies and tools that can aid to build a bridge between the different disciplines in order to develop more mechanistic pharmacological models. This thesis is structured as follows: Chapter 1 proposes a qualitative modeling strategy which consist on a computational framework to perform simulations of Boolean networks in the R environment and analyze the result of the perturbations on these networks. This framework called SPIDDOR (from Systems Pharmacology for effIcient Drug Development On R) combines the advantages of the parameter-free nature of logical models while providing a good approximation of the qualitative behavior of pharmacological systems, making the use of Boolean networks in SP more accessible to scientist involved in drug development, especially at its early stages. Additionally, this tool has been used to qualitatively evaluate the results of Boolean network models describing pathogenic mechanisms in the autoimmune diseases systemic lupus erythematosus and inflammatory bowel disease. The publications corresponding to these works are added in the Appendix of the thesis. Chapter 2 proposes an optimization technique known as Optimal Control and its application to a PKPD model for the testosterone effects of triptorelin, a synthetic gonadotropin-releasing hormone analog used to induce chemical castration in prostate cancer patients, with the goal of improving the release characteristics of the drug. As the proposed approach is not circumscribed to just this particular problem, the reader will find a comprehensive description of how the critical aspects of defining control variables and selecting the cost functions and constraints were handled. Chapter 3 presents a computational framework based on a stochastic model known as multitype branching process used to explore the dynamic evolution of heterogeneous tumor cell populations. This framework, which also consist on an R package, is called ACESO (from A Cancer Evolution Simulation Optimizer) and incorporates pharmacokinetics and drug interaction effects into the stochastic model. The aim of this tool is to identify optimum dosing schedules that minimize the risk of developing resistance to anticancer therapies. Finally, in Chapter 4 a semi-mechanistic model describing the time course of several circulating biomarkers in advanced melanoma patients treated with adjuvant high-dose interferon α2b is presented in order to evaluate the dynamics of the tumor markers as prognostic factors of the overall survival and progression-free survival of the patients. This treatment-biomarker-survival model is also coupled to another semimechanistic model describing the side effects of interferon therapy in the absolute neutrophil counts of the patients in order to simultaneously analyze the benefits and toxic effects of this treatment

Optimal dynamic control approach in a multi-objective therapeutic scenario: application to drug delivery in the treatment of prostate cancer

Numerous problems encountered in computational biology can be formulated as optimization problems. In this context, optimization of drug release characteristics or dosing schedules for anticancer agents has become a prominent area not only for the development of new drugs, but also for established drugs. However, in complex systems, optimization of drug exposure is not a trivial task and cannot be efficiently addressed through trial-error simulation exercises. Finding a solution to those problems is a challenging task which requires more advanced strategies like optimal control theory. In this work, we perform an optimal control analysis on a previously developed computational model for the testosterone effects of triptorelin in prostate cancer patients with the goal of finding optimal drug-release characteristics. We demonstrate how numerical control optimization of non-linear models can be used to find better therapeutic approaches in order to improve the final outcome of the patients

Predicting circulating biomarker response and its impact on the survival of advanced melanoma patients treated with adjuvant therapy

Advanced melanoma remains a disease with poor prognosis. Several serologic markers have been investigated to help monitoring and prognostication, but to date only lactate dehydrogenase (LDH) has been validated as a standard prognostic factor biomarker for this disease by the American Joint Committee on Cancer. In this work, we built a semi-mechanistic model to explore the relationship between the time course of several circulating biomarkers and overall or progression free survival in advanced melanoma patients treated with adjuvant high-dose interferon-[Formula: see text]. Additionally, due to the adverse interferon tolerability, a semi-mechanistic model describing the side effects of the treatment in the absolute neutrophil counts is proposed in order to simultaneously analyze the benefits and toxic effects of this treatment. The results of our analysis suggest that the relative change from baseline of LDH was the most significant predictor of the overall survival of the patients. Unfortunately, there was no significant difference in the proportion of patients with elevated serum biomarkers between the patients who recurred and those who remained free of disease. Still, we believe that the modelling framework presented in this work of circulating biomarkers and adverse effects could constitute an additional strategy for disease monitoring in advance melanoma patients

Beyond Deterministic Models in Drug Discovery and Development

The model-informed drug discovery and development paradigm is now well established among the pharmaceutical industry and regulatory agencies. This success has been mainly due to the ability of pharmacometrics to bring together different modeling strategies, such as population pharmacokinetics/pharmacodynamics (PK/PD) and systems biology/pharmacology. However, there are promising quantitative approaches that are still seldom used by pharmacometricians and that deserve consideration. One such case is the stochastic modeling approach, which can be important when modeling small populations because random events can have a huge impact on these systems. In this review, we aim to raise awareness of stochastic models and how to combine them with existing modeling techniques, with the ultimate goal of making future drug–disease models more versatile and realistic

Advanced Boolean modeling of biological networks applied to systems pharmacology

Motivation Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets. Results In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets

A systems pharmacology model for inflammatory bowel disease

Motivation The literature on complex diseases is abundant but not always quantitative. This is particularly so for Inflammatory Bowel Disease (IBD), where many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. We propose the elaboration and validation of a logic network for IBD able to capture the information available in the literature that will facilitate the identification/validation of therapeutic targets. Results In this article, we propose a logic model for Inflammatory Bowel Disease (IBD) which consists of 43 nodes and 298 qualitative interactions. The model presented is able to describe the pathogenic mechanisms of the disorder and qualitatively describes the characteristic chronic inflammation. A perturbation analysis performed on the IBD network indicates that the model is robust. Also, as described in clinical trials, a simulation of anti-TNFα, anti-IL2 and Granulocyte and Monocyte Apheresis showed a decrease in the Metalloproteinases node (MMPs), which means a decrease in tissue damage. In contrast, as clinical trials have demonstrated, a simulation of anti-IL17 and anti-IFNγ or IL10 overexpression therapy did not show any major change in MMPs expression, as corresponds to a failed therapy. The model proved to be a promising in silico tool for the evaluation of potential therapeutic targets, the identification of new IBD biomarkers, the integration of IBD polymorphisms to anticipate responders and non-responders and can be reduced and transformed in quantitative model/s

A systems pharmacology model for inflammatory bowel disease

<div><p>Motivation</p><p>The literature on complex diseases is abundant but not always quantitative. This is particularly so for Inflammatory Bowel Disease (IBD), where many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. We propose the elaboration and validation of a logic network for IBD able to capture the information available in the literature that will facilitate the identification/validation of therapeutic targets.</p><p>Results</p><p>In this article, we propose a logic model for Inflammatory Bowel Disease (IBD) which consists of 43 nodes and 298 qualitative interactions. The model presented is able to describe the pathogenic mechanisms of the disorder and qualitatively describes the characteristic chronic inflammation. A perturbation analysis performed on the IBD network indicates that the model is robust. Also, as described in clinical trials, a simulation of anti-TNFα, anti-IL2 and Granulocyte and Monocyte Apheresis showed a decrease in the Metalloproteinases node (MMPs), which means a decrease in tissue damage. In contrast, as clinical trials have demonstrated, a simulation of anti-IL17 and anti-IFNγ or IL10 overexpression therapy did not show any major change in MMPs expression, as corresponds to a failed therapy. The model proved to be a promising <i>in silico</i> tool for the evaluation of potential therapeutic targets, the identification of new IBD biomarkers, the integration of IBD polymorphisms to anticipate responders and non-responders and can be reduced and transformed in quantitative model/s.</p></div